Wow! What a year 2016 has been. The big data industry has significant inertia moving into 2017. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. Enjoy!
Last week saw two compelling local big data events here in So Cal, both sponsored by MapR. I thought I’d provide a short recap of the events for those who were unable to attend. I was on a panel for the first event, “Digital Transformation in Big Data” and the discussion revolved around MapR’s unique vision for the “3 Keys to Digital Transformation.” For a detailed discussion, these points are well described in a recent blog post.
Our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors.
In this special guest feature, John Thuma, Director of Analytics at Teradata, shares his five “desired” and five “undesireable” traits to look for in CDOs. He argues that CDOs need to have an ego, have been a data science practitioner and a gamer. Undesirable traits include not being a DIYer, being a cowardly lion and being nepotistic in building teams.
A recent query using Google Trends shows an interesting level of interest in machine learning over time (see figure below). There was an emergence in hype around the 2005 time-frame and led to a cooling off period, but once big data started heating up around 2010, the upward swing in interest continues until today.
In this special guest feature, Abdul Razack, SVP of Platforms, Big Data and Analytics at Infosys, discusses how companies are drowning in data overload and are unable to tap into big data reserves to yield maximum benefits.
Data lakes are enterprise-wide data management platforms designed for storing and analyzing vast amounts of information from disparate data sources in their native format. The idea is to place data into a data lake in their native structure instead of a repository built for a specific purpose such as a data warehouse or data mart.